import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

lowerCamelCase__  =					16
lowerCamelCase__  =					32


def 				lowerCAmelCase__       (			a__    ,     a__ = 16 )      ->Dict:




  '''simple docstring'''

  _UpperCamelCase           =    AutoTokenizer.from_pretrained("bert-base-cased" )
  _UpperCamelCase           =    load_dataset("glue"    ,     "mrpc" )

  def tokenize_function(a__ ):
    # max_length=None => use the model max length (it's actually the default)
    _UpperCamelCase           =    tokenizer(examples["sentence1"]    ,     examples["sentence2"]    ,     truncation=a__    ,     max_length=a__ )
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  # starting with the main process first:
  with accelerator.main_process_first():
    _UpperCamelCase           =    datasets.map(
        a__    ,     batched=a__    ,     remove_columns=["idx", "sentence1", "sentence2"]    ,     )

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  _UpperCamelCase           =    tokenized_datasets.rename_column("label"    ,     "labels" )

  def collate_fn(a__ ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    _UpperCamelCase           =    128 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      _UpperCamelCase           =    16
    elif accelerator.mixed_precision != "no":
      _UpperCamelCase           =    8
    else:
      _UpperCamelCase           =    None

    return tokenizer.pad(
        a__    ,     padding="longest"    ,     max_length=a__    ,     pad_to_multiple_of=a__    ,     return_tensors="pt"    ,     )

  # Instantiate dataloaders.
  _UpperCamelCase           =    DataLoader(
      tokenized_datasets["train"]    ,     shuffle=a__    ,     collate_fn=a__    ,     batch_size=a__ )
  _UpperCamelCase           =    DataLoader(
      tokenized_datasets["validation"]    ,     shuffle=a__    ,     collate_fn=a__    ,     batch_size=a__ )

  return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
   from accelerate.test_utils.training import mocked_dataloaders

   lowerCamelCase__  =					mocked_dataloaders  # noqa: F811


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->Tuple:




  '''simple docstring'''

  if os.environ.get("TESTING_MOCKED_DATALOADERS"    ,     a__ ) == "1":
    _UpperCamelCase           =    2
  # Initialize Accelerator

  # New Code #
  # We pass in "all" to `log_with` to grab all available trackers in the environment
  # Note: If using a custom `Tracker` class, should be passed in here such as:
  # >>> log_with = ["all", MyCustomTrackerClassInstance()]
  if args.with_tracking:
    _UpperCamelCase           =    Accelerator(
        cpu=args.cpu    ,     mixed_precision=args.mixed_precision    ,     log_with="all"    ,     project_dir=args.project_dir )
  else:
    _UpperCamelCase           =    Accelerator(cpu=args.cpu    ,     mixed_precision=args.mixed_precision )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _UpperCamelCase           =    config["lr"]
  _UpperCamelCase           =    int(config["num_epochs"] )
  _UpperCamelCase           =    int(config["seed"] )
  _UpperCamelCase           =    int(config["batch_size"] )
  set_seed(a__ )

  _UpperCamelCase      ,						_UpperCamelCase           =    get_dataloaders(a__    ,     a__ )
  _UpperCamelCase           =    evaluate.load("glue"    ,     "mrpc" )

  # If the batch size is too big we use gradient accumulation
  _UpperCamelCase           =    1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    _UpperCamelCase           =    batch_size // MAX_GPU_BATCH_SIZE
    _UpperCamelCase           =    MAX_GPU_BATCH_SIZE

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _UpperCamelCase           =    AutoModelForSequenceClassification.from_pretrained("bert-base-cased"    ,     return_dict=a__ )

  # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
  # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
  # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
  _UpperCamelCase           =    model.to(accelerator.device )

  # Instantiate optimizer
  _UpperCamelCase           =    AdamW(params=model.parameters()    ,     lr=a__ )

  # Instantiate scheduler
  _UpperCamelCase           =    get_linear_schedule_with_warmup(
      optimizer=a__    ,     num_warmup_steps=100    ,     num_training_steps=(len(a__ ) * num_epochs) // gradient_accumulation_steps    ,     )

  # Prepare everything
  # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
  # prepare method.
  _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    accelerator.prepare(
      a__    ,     a__    ,     a__    ,     a__    ,     a__ )

  # New Code #
  # We need to initialize the trackers we use. Overall configurations can also be stored
  if args.with_tracking:
    _UpperCamelCase           =    os.path.split(a__ )[-1].split("." )[0]
    accelerator.init_trackers(a__    ,     a__ )

  # Now we train the model
  for epoch in range(a__ ):
    model.train()
    # New Code #
    # For our tracking example, we will log the total loss of each epoch
    if args.with_tracking:
      _UpperCamelCase           =    0
    for step, batch in enumerate(a__ ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device )
      _UpperCamelCase           =    model(**a__ )
      _UpperCamelCase           =    outputs.loss
      # New Code #
      if args.with_tracking:
        total_loss += loss.detach().float()
      _UpperCamelCase           =    loss / gradient_accumulation_steps
      accelerator.backward(a__ )
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

    model.eval()
    for step, batch in enumerate(a__ ):
      # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
      batch.to(accelerator.device )
      with torch.no_grad():
        _UpperCamelCase           =    model(**a__ )
      _UpperCamelCase           =    outputs.logits.argmax(dim=-1 )
      _UpperCamelCase      ,						_UpperCamelCase           =    accelerator.gather_for_metrics((predictions, batch["labels"]) )
      metric.add_batch(
          predictions=a__    ,     references=a__    ,     )

    _UpperCamelCase           =    metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'epoch {epoch}:'    ,     a__ )

    # New Code #
    # To actually log, we call `Accelerator.log`
    # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
    if args.with_tracking:
      accelerator.log(
          {
              "accuracy": eval_metric["accuracy"],
              "f1": eval_metric["f1"],
              "train_loss": total_loss.item() / len(a__ ),
              "epoch": epoch,
          }    ,     step=a__    ,     )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
  if args.with_tracking:
    accelerator.end_training()


def 				lowerCAmelCase__       (			)      ->List[str]:




  '''simple docstring'''

  _UpperCamelCase           =    argparse.ArgumentParser(description="Simple example of training script." )
  parser.add_argument(
      "--mixed_precision"    ,     type=a__    ,     default=a__    ,     choices=["no", "fp16", "bf16", "fp8"]    ,     help="Whether to use mixed precision. Choose"
      "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
      "and an Nvidia Ampere GPU."    ,     )
  parser.add_argument("--cpu"    ,     action="store_true"    ,     help="If passed, will train on the CPU." )
  parser.add_argument(
      "--with_tracking"    ,     action="store_true"    ,     help="Whether to load in all available experiment trackers from the environment and use them for logging."    ,     )
  parser.add_argument(
      "--project_dir"    ,     type=a__    ,     default="logs"    ,     help="Location on where to store experiment tracking logs` and relevent project information"    ,     )
  _UpperCamelCase           =    parser.parse_args()
  _UpperCamelCase           =    {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  training_function(a__    ,     a__ )


if __name__ == "__main__":
   main()





def 				lowerCAmelCase__       (			a__ )      ->int:




  '''simple docstring'''

  if not isinstance(a__    ,     a__ ) or number < 0:
    raise ValueError("Input must be a non-negative integer" )

  _UpperCamelCase           =    0
  while number:
    # This way we arrive at next set bit (next 1) instead of looping
    # through each bit and checking for 1s hence the
    # loop won't run 32 times it will only run the number of `1` times
    number &= number - 1
    count += 1
  return count


if __name__ == "__main__":
   import doctest

   doctest.testmod()




